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    <article id="post-Spring开发/稻草问答/稻草问答的学习：一、" class="h-entry article article-type-post" itemprop="blogPost" itemscope itemtype="https://schema.org/BlogPosting">
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    <article id="post-神经网络/导数、梯度及矩阵运算" class="h-entry article article-type-post" itemprop="blogPost" itemscope itemtype="https://schema.org/BlogPosting">
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        <h1 id="课程名称∶导数、梯度及矩阵运算"><a href="#课程名称∶导数、梯度及矩阵运算" class="headerlink" title="课程名称∶导数、梯度及矩阵运算"></a>课程名称∶导数、梯度及矩阵运算</h1><h2 id="一、实验介绍"><a href="#一、实验介绍" class="headerlink" title="一、实验介绍"></a>一、实验介绍</h2><h3 id="1-1实验内容"><a href="#1-1实验内容" class="headerlink" title="1.1实验内容"></a>1.1实验内容</h3><ul>
<li>虽然在实验一中我想尽量少的引入(会让人放弃继续学习的）数学概念，但我似乎还是失败了。不过这几乎是没有办法的事，要想真正学会深度学习，没有一定的数学基础（高等数学、线性代数、概率论、信息论等），(几乎）是不可能的。学深度学习不学其中的原理你可能能够学会搭建模型，但当模型出了问题或者无法训练出好的结果时，不懂原理是很难调试的。<h3 id="1-2实验知识点"><a href="#1-2实验知识点" class="headerlink" title="1.2实验知识点"></a>1.2实验知识点</h3></li>
<li>导数、偏导、梯度、链式法则</li>
<li>矩阵运算基本法则</li>
<li>NumPy基本运算介绍<h3 id="1-3实验环境"><a href="#1-3实验环境" class="headerlink" title="1.3实验环境"></a>1.3实验环境</h3></li>
<li>Python3</li>
<li>NumPy<h2 id="二、实验步骤"><a href="#二、实验步骤" class="headerlink" title="二、实验步骤"></a>二、实验步骤</h2><h3 id="2-1导数、偏导、梯度、复合函数求导链式法则"><a href="#2-1导数、偏导、梯度、复合函数求导链式法则" class="headerlink" title="2.1导数、偏导、梯度、复合函数求导链式法则"></a>2.1导数、偏导、梯度、复合函数求导链式法则</h3><h4 id="2-1-1函数值随自变量的变化速率—导数"><a href="#2-1-1函数值随自变量的变化速率—导数" class="headerlink" title="2.1.1函数值随自变量的变化速率—导数"></a>2.1.1函数值随自变量的变化速率—导数</h4></li>
<li>高中数学里面我们已经学过，函数值随自变星的变化速率是导数。导数衡量的，其实是一个变量对函数值影响能力的大小。导数值越大，则该变量每改变一点对最终函数值的影响越大。且导数值为正时，代表自变量增大时函数值增大，反之若导数值为负，则自变量增大时函数值减小。常见函数的导函数︰<br>原函数f    导函数f’<br>任何常数    0<br>x    1<br>ehx    ehx<br>x^2    2*x<br>1/x    -1/x^2<br>ln(x)    1/x</li>
</ul>
<h4 id="2-1-2从单变量到多变量—偏导"><a href="#2-1-2从单变量到多变量—偏导" class="headerlink" title="2.1.2从单变量到多变量—偏导"></a>2.1.2从单变量到多变量—偏导</h4><ul>
<li>上面我们列举的都是只有一个自变量的函数，如果自变量有多个，如何求导数呢?比如对于函数f=x+y，怎样衡量×和y 分别对函数值f的影响快慢呢﹖数学上引入了偏导的概念，对一个多变量函数f，求f对其中一个自变量x的偏导很简单，就是将与×无关的其他自变量视为常量，再使用单变星求导的方法去求导。得到的即为f对x的偏导。比如∶</li>
<li>令f=x+2y,则f对×求偏导的结果为1，对y 求偏导的结果为2。令f=x*y,则f对x求偏导结果为y，对y求偏导结果为x。<h4 id="2-1-3多变量函数变化最快的方向—梯度"><a href="#2-1-3多变量函数变化最快的方向—梯度" class="headerlink" title="2.1.3多变量函数变化最快的方向—梯度"></a>2.1.3多变量函数变化最快的方向—梯度</h4></li>
<li>2.1.1中我们提到了，对单变量函数来说，导数值的正负代表自变量对函数值影响的“方向”:变大或变小。那对于多变量函数来说，如何表达这个方向呢?这就引入了梯度的概念∶</li>
<li>梯度是一个向量，向星长度与自变量的个数相等，且其中的每一个元素为函数对于对应变星求偏导的值。</li>
<li>比如对于函数f=x*y,其梯度向量为(y,x)，对于具体的自变量的值，比如x=1,y=1的点，其梯度向量就为(1,1)，又比如x=10,y=-20点，其梯度向量就为(-20,18)。</li>
<li>梯度作为一个向量，指向的是使函数值增大最快的方向(回想第一次实验中的损失函数图，梯度所指的方向是向上的)。<h4 id="2-1-4复合函数求导链式法则"><a href="#2-1-4复合函数求导链式法则" class="headerlink" title="2.1.4复合函数求导链式法则"></a>2.1.4复合函数求导链式法则</h4></li>
<li><p>上面我们讲的求导数和求偏导，都是对于“简单函数”，对于“复合函数”，比如下面这样的函数︰<br>f1(x)=1/x<br>f2(x)=eox<br>f=f1(f2(x))</p>
</li>
<li><p>f函数是一个复合函数，它由f1和f2函数“串联”而来。其中f1的输入是f2的输出。</p>
</li>
<li>对于复合函数求导，一种方法是将复合函数展开，比如对于上面的函数，得到f-1/(e^x)，然后再根据简单函数求导法则对自变星求导。过程如下∶f’ = -1/(e<em>x)^2)</em>((ex) ‘) = -(e^x)/((ex)^2) = -1/(e^x)即f’ = -1/(ex)其实，在上面的求导过程中，我们已经使用了求导链式法则(chain rule)，只是你没有察觉而已。求导链式法则让我们可以一部分一部分地，对复合函数求导，而不用放在一起求。这对于编程来说十分重要，它使得对复合函数求导变得十分简单。但是这里描述起来可能稍显复杂。以f为例，当我们需要对自变量x求导时，我们可以先将f2(x)看做一个自变量f2，先让f1对f2求导，得到第一部分导函数-1/(f22)，再让f2对x求导，得到第二部分导函数e^x。求好之后，直接将两部分导数乘起来，即得到最终复合函数整体的导数。不过要先使用实际的表达式替换掉第一部分导数中的f2,即第一部分导数为-1/((e~x)”2)，第二部分导数为e^x。两部分乘起来就得到了最终正确的-1/(e^x)。现在你可能觉得这个链式法则是复杂乏味的，但是下一次实验你会发现链式法则真是太强大了。实际上，我们最后实现的深度神经网络，就是不断在运用求导链式法则。<h3 id="2-2矩阵及其基本运算性质"><a href="#2-2矩阵及其基本运算性质" class="headerlink" title="2.2矩阵及其基本运算性质"></a>2.2矩阵及其基本运算性质</h3></li>
<li>如果你上过本科线性代数课程，你十有八九会对矩阵没有什么感觉，甚至对这么一个运算法则十分奇怪的东西感到厌恶。但我希望你今后能改变对矩阵、对线性代数的看法，不要让糟糕的教材和老师糟糕的 ppt 毁掉线性代数可能带给你的巨大的提升自己(是的，这并不夸张）的机会。矩阵其实非常非常非常有用，在现代科学的每一个角落，几乎都能看到矩阵的身影，深度学习中更是如此。<h4 id="2-2-1矩阵的表达形式"><a href="#2-2-1矩阵的表达形式" class="headerlink" title="2.2.1矩阵的表达形式"></a>2.2.1矩阵的表达形式</h4></li>
<li>一个m*n的矩阵为一个m行n列的数组，比比如︰</li>
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    <article id="post-数据库/第24篇：如何正确的使⽤索引？" class="h-entry article article-type-post" itemprop="blogPost" itemscope itemtype="https://schema.org/BlogPosting">
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        <ul>
<li><p>b+树结构如下：</p>
<ul>
<li>1.叶⼦节点（最下⾯的⼀层）存储关键字（索引字段的值）信息及对应的data，叶<br>⼦节点存储了所有记录的关键字信息</li>
<li>2.其他⾮叶⼦节点只存储关键字的信息及⼦节点的指针<br><img src="image-20220210205025832.png" alt="image-20220210205025832" style="zoom:80%;" /></li>
</ul>
</li>
<li><p>通常说的这个查询⾛索引了是什么意思？</p>
<ul>
<li>当我们对某个字段的值进⾏某种检索的时候，如果这个检索过程中，我们能够快速定位到⽬标数据所在的页，有效的降低页的io操作，⽽不需要去扫描所有的数据页的时候，我们认为这种情况能够有效的利⽤索引，也称这个检索可以⾛索引，如果这个过程中不能够确定数据在那些页中，我们认为这种情况下索引对这个查询是⽆效的，此查询不⾛索引。</li>
</ul>
</li>
</ul>
<h2 id="b-树中数据检索过程"><a href="#b-树中数据检索过程" class="headerlink" title="b+树中数据检索过程"></a>b+树中数据检索过程</h2>
      
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    <article id="post-python/opencv" class="h-entry article article-type-post" itemprop="blogPost" itemscope itemtype="https://schema.org/BlogPosting">
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        <h3 id="python——opencv基础"><a href="#python——opencv基础" class="headerlink" title="python——opencv基础"></a>python——opencv基础</h3><h4 id="2-1-图像的表示"><a href="#2-1-图像的表示" class="headerlink" title="2.1 图像的表示"></a>2.1 图像的表示</h4><p><img src="20180416161025665" alt="这里写图片描述"></p>
<h4 id="2-2-基本处理"><a href="#2-2-基本处理" class="headerlink" title="2.2 基本处理"></a>2.2 基本处理</h4><p><strong>（1）读取图像：cv.imread()</strong></p>
<p>可以按照不同模式读取，一般最常用到的是读取单通道灰度图，或者直接默认读取多通道</p>
<p><strong>（2）储存图像：cv.imwrite()</strong></p>
<p><strong>（2）缩放、裁剪、补边</strong></p>
<ul>
<li>缩放：im.resize()</li>
<li>裁剪：利用array下标截取实现</li>
</ul>

      
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        <h1 id="python读取excel的方法"><a href="#python读取excel的方法" class="headerlink" title="python读取excel的方法"></a>python读取excel的方法</h1><h2 id="读取文件"><a href="#读取文件" class="headerlink" title="读取文件"></a>读取文件</h2><p>一、用xlrd和xlwt读写excel<br>　　　　首先下载安装xlrd和xlwt这两个库。</p>
<p>　　1、打开excel</p>
<p>　　　　readbook = xlrd.open_workbook(r’\test\canying.xlsx’)<br>　　2、获取读入的文件的sheet</p>
<p>　　　　sheet = readbook.sheet_by_index(1)#索引的方式，从0开始<br>　　　　sheet = readbook.sheet_by_name(‘sheet2’)#名字的方式<br>　　3、获取sheet的最大行数和列数</p>
<p>　　　　nrows = sheet.nrows#行<br>　　　　ncols = sheet.ncols#列<br>　　4、获取某个单元格的值</p>
<p>　　　　lng = table.cell(i,3).value#获取i行3列的表格值<br>　　　　lat = table.cell(i,4).value#获取i行4列的表格值<br>　　5、打开将写的表并添加sheet</p>
<p>　　　　writebook = xlwt.Workbook()#打开一个excel<br>　　　　sheet = writebook.add_sheet(‘test’)#在打开的excel中添加一个sheet<br>　　6、将数据写入excel</p>
<p> 　　　　sheet.write(i,0,result[0])#写入excel，i行0列<br> 　　　　sheet.write(i,1,result[1])<br>　　7、保存</p>
<p>　　　　 writebook.save(‘answer.xls’)#一定要记得保存</p>

      
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        <h1 id="用xxx写一个xxx"><a href="#用xxx写一个xxx" class="headerlink" title="用xxx写一个xxx"></a>用xxx写一个xxx</h1><p>最近在学xxx，想做点什么来练练手，xxxx一般是练手项目，但是一时之间找不到别的，就先做个xxx练简单的语法。</p>
<p>要求：xxx</p>
<p>Python版本：xxx</p>
<p>系统环境：xxx</p>
<p>类：</p>
<p>　　xxxxxxxxxxx:xxxxx</p>
<p>源码</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"></span><br></pre></td></tr></table></figure>
<p>笔记：</p>
<p>　　1.Python 没有Switch case语句，可以利用dirt来实现</p>
<p>　　2.Python的=号是复制，复制引用，深复制需要使用copy的deepcopy()函数来实现</p>
<p>　　3.即使在成员函数内，也需要使用self来访问成员变量，这和C++、JAVA很不一样</p>
<p><strong>2017.4.11 更新</strong></p>

      
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